Knowledge-Based Energy Functions for Computational Studies of Proteins

  • Xiang Li
  • Jie Liang


This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design.We discuss in some detail the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and nonlinear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.


Potential Function Protein Structure Prediction Residue Type Residue Pair Cargo Protein 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  • Xiang Li
  • Jie Liang

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